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Brain Tissue Entropy Changes in Patients with Autism Spectrum Disorder

  • Sudhakar TummalaEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 31)

Abstract

Autism Spectrum Disorder (ASD) is accompanied by brain tissue changes in areas that control behavior, cognition, and motor functions, deficient in the disorder. The objective of this research was to evaluate brain structural changes in ASD patients compared to control subjects using voxel-by-voxel image entropy from T1-weighted imaging data of 115 ASD and 105 control subjects from autism brain imaging data exchange. For all subjects, entropy maps were calculated, normalized to a common space and smoothed. Then, the entropy maps were compared at each voxel between groups using analysis of covariance (covariates; age, gender). Increased entropy in ASD patients, indicating chronic injury, emerged in several vital regions including frontal temporal and parietal lobe regions, corpus callosum, cingulate cortices, and hippocampi. Entropy procedure showed significant effect size and demonstrated wide-spread changes in sites that control social behavior, cognitive, and motor activities, suggesting severe damage in those areas. The neuropathological mechanisms contributing to tissue injury remain unclear and possibly due to factors including genetic, atypical early brain growth during childhood.

Keywords

Magnetic resonance imaging Entropy Autism spectrum disorder 

Notes

Acknowledgements

I would like to thank Autism Brain Imaging Data Exchange for providing demographic as well as MRI data for this study.

References

  1. 1.
    Rapin I (1997) Autism. N Engl J Med 337:97–104CrossRefGoogle Scholar
  2. 2.
    Geschwind DH, Levitt P (2007) Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 17:103–111CrossRefGoogle Scholar
  3. 3.
    Yang DY, Beam D, Pelphrey KA, Abdullahi S, Jou RJ (2016) Cortical morphological markers in children with autism: a structural magnetic resonance imaging study of thickness, area, volume, and gyrification. Mol Autism 7:11CrossRefGoogle Scholar
  4. 4.
    Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, Reiss AL (2004) White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry 55:323–326CrossRefGoogle Scholar
  5. 5.
    Alexander AL, Lee JE, Lazar M, Boudos R, DuBray MB, Oakes TR, Miller JN, Lu J, Jeong EK, McMahon WM, Bigler ED, Lainhart JE (2007) Diffusion tensor imaging of the corpus callosum in Autism. Neuroimage 34:61–73CrossRefGoogle Scholar
  6. 6.
    Ismail MM, Keynton RS, Mostapha MM, ElTanboly AH, Casanova MF, Gimel’farb GL, El-Baz A (2016) Studying autism spectrum disorder with structural and diffusion magnetic resonance imaging: a survey. Front Hum Neurosci 10:211CrossRefGoogle Scholar
  7. 7.
    Maani R, Yang YH, Kalra S (2015) Voxel-based texture analysis of the brain. PLoS One 10:e0117759CrossRefGoogle Scholar
  8. 8.
    Sikio M, Holli-Helenius KK, Harrison LC, Ryymin P, Ruottinen H, Saunamaki T, Eskola HJ, Elovaara I, Dastidar P (2015) MR image texture in Parkinson’s disease: a longitudinal study. Acta Radiol 56:97–104CrossRefGoogle Scholar
  9. 9.
    Kjaer L, Ring P, Thomsen C, Henriksen O (1995) Texture analysis in quantitative MR imaging. Tissue characterisation of normal brain and intracranial tumours at 1.5 T. Acta Radiol 36:127–135 (1995)CrossRefGoogle Scholar
  10. 10.
    Michoux N, Guillet A, Rommel D, Mazzamuto G, Sindic C, Duprez T (2015) Texture analysis of T2-weighted MR images to assess acute inflammation in brain MS lesions. PLoS One 10:e0145497CrossRefGoogle Scholar
  11. 11.
    Chaddad A, Desrosiers C, Toews M (2017) Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Sci Rep 7:45639CrossRefGoogle Scholar
  12. 12.
    Radulescu E, Ganeshan B, Minati L, Beacher FD, Gray MA, Chatwin C, Young RC, Harrison NA, Critchley HD (2013) Gray matter textural heterogeneity as a potential in-vivo biomarker of fine structural abnormalities in Asperger syndrome. Pharmacogenom J 13:70–79CrossRefGoogle Scholar
  13. 13.
    Diagnostic and statistical manual of mental disorders. American Psychiatric Association, Washington DC test revision (2000)Google Scholar
  14. 14.
    Wechsler D (1999) Wechsler abbreviated scale of intelligence psychological corporation. San Antonio, TXGoogle Scholar
  15. 15.
    Wechsler D (1997) WAIS-III: Wechsler adult intelligence scale. Psychological Corporation, San Antonio, TXGoogle Scholar
  16. 16.
    Wechsler D (2003) Wechsler intelligence scale for children San Antonio, 4th ed. TX, Psychological CorporationGoogle Scholar
  17. 17.
    Rorden C, Karnath HO, Bonilha L (2007) Improving lesion-symptom mapping. J Cogn Neurosci 19:1081–1088CrossRefGoogle Scholar
  18. 18.
    Travers BG, Tromp do PM, Adluru N, Lange N, Destiche D, Ennis C, Nielsen JA, Froehlich AL, Prigge MB, Fletcher PT, Anderson JS, Zielinski BA, Bigler ED, Lainhart JE, Alexander AL (2015) Atypical development of white matter microstructure of the corpus callosum in males with autism: a longitudinal investigation. Mol Autism 6:15CrossRefGoogle Scholar
  19. 19.
    Parellada M, Penzol MJ, Pina L, Moreno C, Gonzalez-Vioque E, Zalsman G, Arango C (2014) The neurobiology of autism spectrum disorders. Eur Psychiatr 29:11–19CrossRefGoogle Scholar
  20. 20.
    Libero LE, Reid MA, White DM, Salibi N, Lahti AC, Kana RK (2016) Biochemistry of the cingulate cortex in autism: an MR spectroscopy study. Autism Res 9:643–657CrossRefGoogle Scholar
  21. 21.
    Aoki Y, Kasai K, Yamasue H (2012) Age-related change in brain metabolite abnormalities in autism: a meta-analysis of proton magnetic resonance spectroscopy studies. Trans Psychiatr 2:e69CrossRefGoogle Scholar
  22. 22.
    Friedman SD, Shaw DW, Artru AA, Dawson G, Petropoulos H, Dager SR (2006) Gray and white matter brain chemistry in young children with autism. Arch Gen Psychiatr 63:786–794CrossRefGoogle Scholar
  23. 23.
    Libero LE, Burge WK, Deshpande HD, Pestilli F, Kana RK (2016) White matter diffusion of major fiber tracts implicated in autism spectrum disorder. Brain Connect 6:691–699CrossRefGoogle Scholar
  24. 24.
    Fozouni N, Chopp M, Nejad-Davarani SP, Zhang ZG, Lehman NL, Gu S, Ueno Y, Lu M, Ding G, Li L, Hu J, Bagher-Ebadian H, Hearshen D, Jiang Q (2013) Characterizing brain structures and remodeling after TBI based on information content, diffusion entropy. PLoS One 8:e76343CrossRefGoogle Scholar
  25. 25.
    Bloom JS, Hynd GW (2005) The role of the corpus callosum in interhemispheric transfer of information: excitation or inhibition? Neuropsychol Rev 15:59–71CrossRefGoogle Scholar
  26. 26.
    Geib BR, Stanley ML, Dennis NA, Woldorff MG, Cabeza R (2017) From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval. Human Brain Mapp 38:2242–2259CrossRefGoogle Scholar
  27. 27.
    Leech R, Sharp DJ (2014) The role of the posterior cingulate cortex in cognition and disease. Brain 137:12–32CrossRefGoogle Scholar
  28. 28.
    Pertzov Y, Miller TD, Gorgoraptis N, Caine D, Schott JM, Butler C, Husain M (2013) Binding deficits in memory following medial temporal lobe damage in patients with voltage-gated potassium channel complex antibody-associated limbic encephalitis. Brain 136:2474–2485CrossRefGoogle Scholar
  29. 29.
    Sarkheil P, Goebel R, Schneider F, Mathiak K (2013) Emotion unfolded by motion: a role for parietal lobe in decoding dynamic facial expressions. Soc Cogn Affect Neurosci 8:950–957CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSRM University-APAmaravatiIndia

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